Title: Computational Journalism: Social Media Visual Analytics for Journalistic Inquiry
Nicholas Diakopoulos (GA Tech, PhD, 2009).
Date/Day: Thursday 4/15/2010
Location: CCB 102
What is the impact that computing can and will have on the changing
landscape of news production and consumption? In this talk I will
introduce Computational Journalism as the application of computing to
the processes of journalism including information gathering,
organization and sensemaking, communication and presentation, and
dissemination and public interaction with news information, all while
upholding values of journalism such as balance, accuracy, and
objectivity. I will then present recent work related to visual and
analytic tools for helping to enhance journalists’ and consumers’
abilities to make sense of public commentary on televised news events
such as debates and speeches. This work suggests opportunities for
computing to enhance both the ability of journalists to leverage
public response to news events, as well as for the public to have more
meaningful experiences when participating in online news commentary
Nicholas Diakopoulos is a Computing Innovation Fellow at the School of
Communication and Information at Rutgers University. He received his
Ph.D. in Computer Science from the School of Interactive Computing at
the Georgia Institute of Technology in 2009. His research interests
span human computer interaction, information visualization, and
multimedia content analysis with themes from media including
journalism, collaborative authorship and annotation, and games.
Mapping the World’s Photos
Date: Tuesday 4/13/2010
Time: 1:30p – 3:00p.
Location: Klaus 1116 West
We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that combined visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual or visual features alone. We illustrate using these techniques to structure a large collection of geotagged photos, while also revealing various interesting properties about popular cities and landmarks at a global scale.